{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2352944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/collinsliu/jupyter/da_case/ '"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import os\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "os.path.abspath(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f32f939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>DNU_adr</th>\n",
       "      <th>DNU_ios</th>\n",
       "      <th>DAU_OLD_adr</th>\n",
       "      <th>DAU_OLD_ios</th>\n",
       "      <th>ARPPU_adr_old</th>\n",
       "      <th>ARPPU_ios_old</th>\n",
       "      <th>ARPPU_adr_new</th>\n",
       "      <th>ARPPU_ios_new</th>\n",
       "      <th>ARPPU</th>\n",
       "      <th>...</th>\n",
       "      <th>Revenue_adr_new</th>\n",
       "      <th>Revenue_adr_old</th>\n",
       "      <th>Revenue_ios_new</th>\n",
       "      <th>Revenue_ios_old</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>ARPU_adr_new</th>\n",
       "      <th>ARPU_adr_old</th>\n",
       "      <th>ARPU_ios_new</th>\n",
       "      <th>ARPU_ios_old</th>\n",
       "      <th>ARPU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/5/27</td>\n",
       "      <td>16372</td>\n",
       "      <td>4833</td>\n",
       "      <td>36703</td>\n",
       "      <td>89669</td>\n",
       "      <td>2.584007</td>\n",
       "      <td>3.691046</td>\n",
       "      <td>16.675016</td>\n",
       "      <td>20.416214</td>\n",
       "      <td>6.490094</td>\n",
       "      <td>...</td>\n",
       "      <td>33781.78481</td>\n",
       "      <td>5981.795208</td>\n",
       "      <td>15879.98114</td>\n",
       "      <td>29549.32706</td>\n",
       "      <td>85192.88822</td>\n",
       "      <td>2.063388</td>\n",
       "      <td>0.162978</td>\n",
       "      <td>3.285740</td>\n",
       "      <td>0.329538</td>\n",
       "      <td>0.577278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/5/28</td>\n",
       "      <td>13191</td>\n",
       "      <td>5043</td>\n",
       "      <td>37574</td>\n",
       "      <td>91197</td>\n",
       "      <td>2.715940</td>\n",
       "      <td>3.596645</td>\n",
       "      <td>14.396825</td>\n",
       "      <td>19.723658</td>\n",
       "      <td>5.375310</td>\n",
       "      <td>...</td>\n",
       "      <td>18746.02328</td>\n",
       "      <td>5773.081055</td>\n",
       "      <td>12203.22698</td>\n",
       "      <td>30257.91456</td>\n",
       "      <td>66980.24588</td>\n",
       "      <td>1.421122</td>\n",
       "      <td>0.153646</td>\n",
       "      <td>2.419835</td>\n",
       "      <td>0.331786</td>\n",
       "      <td>0.455632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/5/29</td>\n",
       "      <td>14659</td>\n",
       "      <td>4584</td>\n",
       "      <td>36526</td>\n",
       "      <td>96621</td>\n",
       "      <td>2.587766</td>\n",
       "      <td>4.034278</td>\n",
       "      <td>16.989047</td>\n",
       "      <td>19.999810</td>\n",
       "      <td>6.309922</td>\n",
       "      <td>...</td>\n",
       "      <td>28313.01868</td>\n",
       "      <td>5512.379561</td>\n",
       "      <td>16108.31879</td>\n",
       "      <td>37024.50583</td>\n",
       "      <td>86958.22286</td>\n",
       "      <td>1.931443</td>\n",
       "      <td>0.150917</td>\n",
       "      <td>3.514031</td>\n",
       "      <td>0.383193</td>\n",
       "      <td>0.570629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/5/30</td>\n",
       "      <td>15443</td>\n",
       "      <td>4036</td>\n",
       "      <td>29857</td>\n",
       "      <td>102150</td>\n",
       "      <td>2.561293</td>\n",
       "      <td>4.016397</td>\n",
       "      <td>16.891464</td>\n",
       "      <td>20.558770</td>\n",
       "      <td>5.796145</td>\n",
       "      <td>...</td>\n",
       "      <td>22250.85524</td>\n",
       "      <td>4483.620253</td>\n",
       "      <td>15663.29888</td>\n",
       "      <td>45557.62091</td>\n",
       "      <td>87955.39528</td>\n",
       "      <td>1.440838</td>\n",
       "      <td>0.150170</td>\n",
       "      <td>3.880897</td>\n",
       "      <td>0.445987</td>\n",
       "      <td>0.580617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/5/31</td>\n",
       "      <td>14644</td>\n",
       "      <td>4818</td>\n",
       "      <td>36358</td>\n",
       "      <td>92986</td>\n",
       "      <td>2.695559</td>\n",
       "      <td>4.049023</td>\n",
       "      <td>14.958776</td>\n",
       "      <td>20.310454</td>\n",
       "      <td>6.383606</td>\n",
       "      <td>...</td>\n",
       "      <td>29885.57085</td>\n",
       "      <td>5518.623523</td>\n",
       "      <td>13219.92047</td>\n",
       "      <td>32307.83014</td>\n",
       "      <td>80931.94498</td>\n",
       "      <td>2.040807</td>\n",
       "      <td>0.151786</td>\n",
       "      <td>2.743861</td>\n",
       "      <td>0.347448</td>\n",
       "      <td>0.543876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  DNU_adr  DNU_ios  DAU_OLD_adr  DAU_OLD_ios  ARPPU_adr_old  \\\n",
       "0  2020/5/27    16372     4833        36703        89669       2.584007   \n",
       "1  2020/5/28    13191     5043        37574        91197       2.715940   \n",
       "2  2020/5/29    14659     4584        36526        96621       2.587766   \n",
       "3  2020/5/30    15443     4036        29857       102150       2.561293   \n",
       "4  2020/5/31    14644     4818        36358        92986       2.695559   \n",
       "\n",
       "   ARPPU_ios_old  ARPPU_adr_new  ARPPU_ios_new     ARPPU  ...  \\\n",
       "0       3.691046      16.675016      20.416214  6.490094  ...   \n",
       "1       3.596645      14.396825      19.723658  5.375310  ...   \n",
       "2       4.034278      16.989047      19.999810  6.309922  ...   \n",
       "3       4.016397      16.891464      20.558770  5.796145  ...   \n",
       "4       4.049023      14.958776      20.310454  6.383606  ...   \n",
       "\n",
       "   Revenue_adr_new  Revenue_adr_old  Revenue_ios_new  Revenue_ios_old  \\\n",
       "0      33781.78481      5981.795208      15879.98114      29549.32706   \n",
       "1      18746.02328      5773.081055      12203.22698      30257.91456   \n",
       "2      28313.01868      5512.379561      16108.31879      37024.50583   \n",
       "3      22250.85524      4483.620253      15663.29888      45557.62091   \n",
       "4      29885.57085      5518.623523      13219.92047      32307.83014   \n",
       "\n",
       "       Revenue  ARPU_adr_new  ARPU_adr_old  ARPU_ios_new  ARPU_ios_old  \\\n",
       "0  85192.88822      2.063388      0.162978      3.285740      0.329538   \n",
       "1  66980.24588      1.421122      0.153646      2.419835      0.331786   \n",
       "2  86958.22286      1.931443      0.150917      3.514031      0.383193   \n",
       "3  87955.39528      1.440838      0.150170      3.880897      0.445987   \n",
       "4  80931.94498      2.040807      0.151786      2.743861      0.347448   \n",
       "\n",
       "       ARPU  \n",
       "0  0.577278  \n",
       "1  0.455632  \n",
       "2  0.570629  \n",
       "3  0.580617  \n",
       "4  0.543876  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_path = \"/Users/collinsliu/learning/jobs/self advance/course/week3/week3-data.csv\"\n",
    "large_df = pd.read_csv(csv_path,\n",
    "                       encoding=\"utf-8\")\n",
    "large_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f669ea",
   "metadata": {},
   "source": [
    "# Question 1 - Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed3fb7c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Revenue_adr_new</th>\n",
       "      <th>Revenue_adr_old</th>\n",
       "      <th>Revenue_ios_new</th>\n",
       "      <th>Revenue_ios_old</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/5/27</td>\n",
       "      <td>33781.78481</td>\n",
       "      <td>5981.795208</td>\n",
       "      <td>15879.98114</td>\n",
       "      <td>29549.32706</td>\n",
       "      <td>85192.88822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/5/28</td>\n",
       "      <td>18746.02328</td>\n",
       "      <td>5773.081055</td>\n",
       "      <td>12203.22698</td>\n",
       "      <td>30257.91456</td>\n",
       "      <td>66980.24588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/5/29</td>\n",
       "      <td>28313.01868</td>\n",
       "      <td>5512.379561</td>\n",
       "      <td>16108.31879</td>\n",
       "      <td>37024.50583</td>\n",
       "      <td>86958.22286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/5/30</td>\n",
       "      <td>22250.85524</td>\n",
       "      <td>4483.620253</td>\n",
       "      <td>15663.29888</td>\n",
       "      <td>45557.62091</td>\n",
       "      <td>87955.39528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/5/31</td>\n",
       "      <td>29885.57085</td>\n",
       "      <td>5518.623523</td>\n",
       "      <td>13219.92047</td>\n",
       "      <td>32307.83014</td>\n",
       "      <td>80931.94498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  Revenue_adr_new  Revenue_adr_old  Revenue_ios_new  \\\n",
       "0  2020/5/27      33781.78481      5981.795208      15879.98114   \n",
       "1  2020/5/28      18746.02328      5773.081055      12203.22698   \n",
       "2  2020/5/29      28313.01868      5512.379561      16108.31879   \n",
       "3  2020/5/30      22250.85524      4483.620253      15663.29888   \n",
       "4  2020/5/31      29885.57085      5518.623523      13219.92047   \n",
       "\n",
       "   Revenue_ios_old      Revenue  \n",
       "0      29549.32706  85192.88822  \n",
       "1      30257.91456  66980.24588  \n",
       "2      37024.50583  86958.22286  \n",
       "3      45557.62091  87955.39528  \n",
       "4      32307.83014  80931.94498  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GMV_df = large_df[[\"date\",\"Revenue_adr_new\",\"Revenue_adr_old\",\"Revenue_ios_new\",\"Revenue_ios_old\",\"Revenue\"]]\n",
    "print(len(GMV_df))\n",
    "GMV_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0604db",
   "metadata": {},
   "source": [
    "## Central - Skedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "604cd94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue_adr_new</th>\n",
       "      <th>Revenue_adr_old</th>\n",
       "      <th>Revenue_ios_new</th>\n",
       "      <th>Revenue_ios_old</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28851.544627</td>\n",
       "      <td>4813.549873</td>\n",
       "      <td>12397.460111</td>\n",
       "      <td>31988.992255</td>\n",
       "      <td>78051.546866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4345.384312</td>\n",
       "      <td>797.597074</td>\n",
       "      <td>2919.023798</td>\n",
       "      <td>5377.386387</td>\n",
       "      <td>7322.083402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17202.190600</td>\n",
       "      <td>3335.119817</td>\n",
       "      <td>6576.507608</td>\n",
       "      <td>20170.586990</td>\n",
       "      <td>61494.175640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26599.796008</td>\n",
       "      <td>4232.747174</td>\n",
       "      <td>10183.149807</td>\n",
       "      <td>28381.412030</td>\n",
       "      <td>73561.493925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28562.811560</td>\n",
       "      <td>4692.394351</td>\n",
       "      <td>12227.875440</td>\n",
       "      <td>32069.645935</td>\n",
       "      <td>78355.352140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31966.120293</td>\n",
       "      <td>5470.515162</td>\n",
       "      <td>14644.976675</td>\n",
       "      <td>34932.691052</td>\n",
       "      <td>83786.470012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39286.785400</td>\n",
       "      <td>6692.816226</td>\n",
       "      <td>20418.782600</td>\n",
       "      <td>52572.171730</td>\n",
       "      <td>93180.927940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Revenue_adr_new  Revenue_adr_old  Revenue_ios_new  Revenue_ios_old  \\\n",
       "count       100.000000       100.000000       100.000000       100.000000   \n",
       "mean      28851.544627      4813.549873     12397.460111     31988.992255   \n",
       "std        4345.384312       797.597074      2919.023798      5377.386387   \n",
       "min       17202.190600      3335.119817      6576.507608     20170.586990   \n",
       "25%       26599.796008      4232.747174     10183.149807     28381.412030   \n",
       "50%       28562.811560      4692.394351     12227.875440     32069.645935   \n",
       "75%       31966.120293      5470.515162     14644.976675     34932.691052   \n",
       "max       39286.785400      6692.816226     20418.782600     52572.171730   \n",
       "\n",
       "            Revenue  \n",
       "count    100.000000  \n",
       "mean   78051.546866  \n",
       "std     7322.083402  \n",
       "min    61494.175640  \n",
       "25%    73561.493925  \n",
       "50%    78355.352140  \n",
       "75%    83786.470012  \n",
       "max    93180.927940  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GMV_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5393e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"font.sans-serif\"] = \"SimHei\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "3a0e8e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(GMV_df[\"Revenue\"])\n",
    "plt.title(\"box plot of GMV\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60c5163",
   "metadata": {},
   "source": [
    "## Tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "63e4131c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82869.42853024241 [-95.40359732]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "rgs = LinearRegression()\n",
    "gmv_x = np.arange(1,len(GMV_df)+1,1)\n",
    "rgs.fit(gmv_x.reshape(-1,1), GMV_df[\"Revenue\"])\n",
    "print(rgs.intercept_, rgs.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "681c9881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gmv_pred = rgs.predict(gmv_x.reshape(-1,1))\n",
    "\n",
    "plt.scatter(gmv_x, GMV_df[\"Revenue\"], marker=\"*\", color='orange')\n",
    "plt.plot(gmv_x, GMV_df[\"Revenue\"],lw=.5,ls='-',color='green',label=\"original\")\n",
    "plt.plot(gmv_x, gmv_pred, lw=.5, ls='--', color='blue',label=\"prediction\")\n",
    "plt.title(\"predictioin of GMV\")\n",
    "plt.yticks(np.arange(50000,105000,5000))\n",
    "plt.xticks(gmv_x[::5],large_df[\"date\"][::5],fontsize=8.5, rotation=45)\n",
    "plt.ylabel(\"GMV\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.legend(loc=\"best\",title=\"Legend\",fontsize=8.5)\n",
    "plt.grid(alpha=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53648026",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "27967376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paysuser    0.769715\n",
       "pay         0.746352\n",
       "Revenue     1.000000\n",
       "ARPU        0.986472\n",
       "Name: Revenue, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df = large_df.copy()\n",
    "corrs = corr_df.corr()\n",
    "corr_gmv = corrs[\"Revenue\"]\n",
    "corr_gmv[corrs[\"Revenue\"]>0.7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ce6511",
   "metadata": {},
   "source": [
    "## Summary\n",
    "1. 首先我们可以看到 GMV的均值为78051.546866，标准差为7322.08340\n",
    "2. 其次，通过线性拟合，我们发现GVM在100天内是总体下降的，服从以下趋势\n",
    "\n",
    "$$ \n",
    "    GMV = 82869.42853024241 -95.40359732 t \n",
    "$$\n",
    "\n",
    "3. 最后，我们发现GMV与以下几个指标有较为密切的关系，即皮尔逊相关系数大于0.7,他们分别是\n",
    "> paysuser <br>\n",
    "> pay <br>\n",
    "> ARPU <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67746620",
   "metadata": {},
   "source": [
    "# Question 2 - Precausion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e31a1143",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_window = 20\n",
    "p_sigma = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "84e52bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_mean = []\n",
    "p_std = []\n",
    "for i in range(p_window+1,len(GMV_df)+1):\n",
    "    p_mean.append(np.mean(GMV_df[\"Revenue\"][i-p_window:i]))\n",
    "    p_std.append(np.std(GMV_df[\"Revenue\"][i-p_window:i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0580ea45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gmv_x, GMV_df[\"Revenue\"], marker=\"*\", \n",
    "            s=10.5,color='orange')\n",
    "plt.plot(gmv_x[p_window:len(GMV_df)+1], \n",
    "         np.array(p_mean)+p_sigma*np.array(p_std), \n",
    "         lw=.5, ls='--', color='blue',\n",
    "         label=r\"$\\mu$+\"+str(p_sigma)+\"$\\sigma$\")\n",
    "plt.plot(gmv_x[p_window:len(GMV_df)+1], \n",
    "         np.array(p_mean),\n",
    "         lw=.5, ls='--', color='green',\n",
    "         label=r\"$\\mu$\")\n",
    "plt.plot(gmv_x[p_window:len(GMV_df)+1], \n",
    "         np.array(p_mean)-p_sigma*np.array(p_std), \n",
    "         lw=.5, ls='--', color='blue',\n",
    "         label=r\"$\\mu$-\"+str(p_sigma)+\"$\\sigma$\")\n",
    "plt.legend(loc=\"best\",title=\"Notification\",fontsize=8)\n",
    "plt.title(r\"$\\sigma$ Principle for Precausion\")\n",
    "plt.ylabel(\"Revenue\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.xticks(gmv_x[::5],GMV_df[\"date\"][::5],fontsize=6.5,rotation=45)\n",
    "plt.grid(alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8f81bb",
   "metadata": {},
   "source": [
    "# Question 3 - Position\n",
    "## Contribution on DAU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "daccffb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>DNU_adr</th>\n",
       "      <th>DNU_ios</th>\n",
       "      <th>DAU_OLD_adr</th>\n",
       "      <th>DAU_OLD_ios</th>\n",
       "      <th>DAU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/5/27</td>\n",
       "      <td>16372</td>\n",
       "      <td>4833</td>\n",
       "      <td>36703</td>\n",
       "      <td>89669</td>\n",
       "      <td>147577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/5/28</td>\n",
       "      <td>13191</td>\n",
       "      <td>5043</td>\n",
       "      <td>37574</td>\n",
       "      <td>91197</td>\n",
       "      <td>147005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/5/29</td>\n",
       "      <td>14659</td>\n",
       "      <td>4584</td>\n",
       "      <td>36526</td>\n",
       "      <td>96621</td>\n",
       "      <td>152390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/5/30</td>\n",
       "      <td>15443</td>\n",
       "      <td>4036</td>\n",
       "      <td>29857</td>\n",
       "      <td>102150</td>\n",
       "      <td>151486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/5/31</td>\n",
       "      <td>14644</td>\n",
       "      <td>4818</td>\n",
       "      <td>36358</td>\n",
       "      <td>92986</td>\n",
       "      <td>148806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  DNU_adr  DNU_ios  DAU_OLD_adr  DAU_OLD_ios     DAU\n",
       "0  2020/5/27    16372     4833        36703        89669  147577\n",
       "1  2020/5/28    13191     5043        37574        91197  147005\n",
       "2  2020/5/29    14659     4584        36526        96621  152390\n",
       "3  2020/5/30    15443     4036        29857       102150  151486\n",
       "4  2020/5/31    14644     4818        36358        92986  148806"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data preparation\n",
    "dau_df = large_df[[\"date\",\"DNU_adr\",\"DNU_ios\",\"DAU_OLD_adr\",\"DAU_OLD_ios\",\"DAU\"]]\n",
    "dau_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "962b2eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'DNU_adr', 'DNU_ios', 'DAU_OLD_adr', 'DAU_OLD_ios', 'DAU'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(dau_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f24a80b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1347,   -13, -3240, -3899, -8499])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s812 = dau_df[dau_df[\"date\"] == \"2020/8/12\"]\n",
    "s813 = dau_df[dau_df[\"date\"] == \"2020/8/13\"]\n",
    "s812 = np.array(s812.drop(\"date\", axis=1)).flatten()\n",
    "s813 = np.array(s813.drop(\"date\", axis=1)).flatten()\n",
    "delta = s812-s813\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0f18c497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.85%\n",
      "0.15%\n",
      "38.12%\n",
      "45.88%\n",
      "100.0%\n"
     ]
    }
   ],
   "source": [
    "base = delta[-1]\n",
    "for item in delta:\n",
    "    print(str(np.round(item/base*100,2)) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16274ea3",
   "metadata": {},
   "source": [
    "从结果来看，DAU_OLD_ios 也就是旧版本IOS对于DAU下降的贡献是最大的"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db30a0f0",
   "metadata": {},
   "source": [
    "## Contribution on GMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "faa9ae03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>DAU</th>\n",
       "      <th>pay</th>\n",
       "      <th>ARPPU</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/5/27</td>\n",
       "      <td>147577</td>\n",
       "      <td>0.088910</td>\n",
       "      <td>6.490094</td>\n",
       "      <td>85192.88822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/5/28</td>\n",
       "      <td>147005</td>\n",
       "      <td>0.084739</td>\n",
       "      <td>5.375310</td>\n",
       "      <td>66980.24588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/5/29</td>\n",
       "      <td>152390</td>\n",
       "      <td>0.090413</td>\n",
       "      <td>6.309922</td>\n",
       "      <td>86958.22286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/5/30</td>\n",
       "      <td>151486</td>\n",
       "      <td>0.100141</td>\n",
       "      <td>5.796145</td>\n",
       "      <td>87955.39528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/5/31</td>\n",
       "      <td>148806</td>\n",
       "      <td>0.085165</td>\n",
       "      <td>6.383606</td>\n",
       "      <td>80931.94498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     DAU       pay     ARPPU      Revenue\n",
       "0  2020/5/27  147577  0.088910  6.490094  85192.88822\n",
       "1  2020/5/28  147005  0.084739  5.375310  66980.24588\n",
       "2  2020/5/29  152390  0.090413  6.309922  86958.22286\n",
       "3  2020/5/30  151486  0.100141  5.796145  87955.39528\n",
       "4  2020/5/31  148806  0.085165  6.383606  80931.94498"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmv_df = large_df[[\"date\",\"DAU\",\"pay\",\"ARPPU\",\"Revenue\"]]\n",
    "gmv_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "a1bb8741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'DAU', 'pay', 'ARPPU', 'Revenue'], dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmv_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "6fc923e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.17437385 -1.15838575  0.8009937   4.81713413]\n",
      "[ 5.18017177 -1.04733597  0.78765582  4.9206832 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.00579792, -0.11104978,  0.01333787, -0.10354907])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s621 = gmv_df[gmv_df[\"date\"] == '2020/6/21']\n",
    "s622 = gmv_df[gmv_df[\"date\"] == '2020/6/22']\n",
    "s621 = np.log10(np.array(s621.drop(\"date\",axis=1)).flatten())\n",
    "print(s621)\n",
    "s622 = np.log10(np.array(s622.drop(\"date\",axis=1)).flatten())\n",
    "print(s622)\n",
    "delta = s621-s622\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "24206d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0%\n",
      "107.0%\n",
      "-13.0%\n",
      "100.0%\n"
     ]
    }
   ],
   "source": [
    "base = delta[-1]\n",
    "for item in delta:\n",
    "    print(str(np.round(item/base,2)*100)+ \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3813909c",
   "metadata": {},
   "source": [
    "从最后的结果来看，我们可以明确的发现转化率的增加直接导致GMV的提升"
   ]
  }
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